基于关系数据库的实时XML数据查询处理

力图用成熟的关系数据库查询机制处理符合DTD的XML文档。提出了一整套数据模型、转换规则和算法描述,可以将XML文档转换为关系元组,从而达到用XML实现基于关系数据库的实时数据一致性查询处理的目的。

基于图的音乐数据查询处理及优化方法

音乐数据之间存在复杂关联关系,适合用图数据结构对其建模并进行查询处理.然而现有的图查询处理算法只关注图结构特征,并未针对音乐元数据和音乐内容数据进行优化,查询效率不高.基于图音乐数据模型GraMM与查询语言GraMQL,提出了基于图的音乐数据查询处理算法.该算法根据音乐数据的特点,使用图结构剪枝、音乐元数据剪枝以及音乐内容剪枝3种策略对搜索空间进行剪枝,提高了查询效率.进而给出了调整查询顶点搜索顺序的优化方法以及基于开销模型的音乐内容剪枝位置优化方法,加快了查询处理速度.实验结果表明所提音乐查询处理及优化算法能高效处理音乐元数据和音乐内容数据查询请求.

用VF6.0建立的椭偏仪数据查询处理系统

主要针对物理实验教学中椭偏仪实验数据处理中存在的问题,简单介绍了椭偏仪数据处理查询系统软件的设计思想和应用功能,该软件用可视化语言Visual FoxPro6.0进行编程,成功地解决了椭偏仪数据处理中的一些困难,也能很好地满足科学研究和实验教学的要求,对面向小型数据库的数据处理系统的开发也有一定的参考价值。

基于MapReduce平台的大数据查询与处理优化算法

基于MapReduce编程并行计算框架、RSJ表连接算法等软件技术,构建了混合式MapReduce并行运算处理系统,通过读取元信息管理表的数据、提取表连接属性信息,按照表数据的属性列进行完整的数据复制与存储,得到合并汇总后完整的共享信息,使用改进RSJ的分布式缓存表连接算法滤除不满足连接条件的数据。仿真实验表明,运用MapReduce并行计算框架和RSJ表连接算法进行数据处理,可减少Map端输出的数据量,缩短网络数据传输与处理时长,实现海量化大数据查询与处理的优化目标。

数据仓库查询处理中的一种多表连接算法

在进行数据仓库的 OLAP( online analytical processing,联机分析处理 )查询处理时 ,经常会涉及到多表连接操作 ,因此 ,提高多表连接的性能就成了数据仓库领域的关键性问题 .基于数据仓库的星型模式 ,给出了一种新的多表连接算法 ( M- Join) .与传统关系数据库管理系统的多表连接查询处理相比 ,该算法充分考虑了数据仓库中的数据本身和多表连接的特点 ,采用对多个表进行一次性连接的方法 ,使得查询的性能有明显的改善 .同时 ,还给出了算法的实验结果和分析 .

IMDBS系统中的多源数据模型、语言及查询处理

ＩＭＤＢＳ是一个集成式的多数据库系统，该系统是通过对已有的局部异构数据库进行信息和数据的集成，为全局用户提供共享的集成信息；同时每个局部应用仍能在被集成后的数据上运行，这样就保证了局部数据库的自治性．ＩＭＤＢＳ系统主要是基于源标签集成机制，并与非一范式（ＮＦ２）模型相结合作为多源数据模型，多源数据语言为ＰＳＱＬ／ＮＦ，它是一个类ＳＱＬ语言，非常方便于用户的使用．该系统具有自下而上集成关系、网状和层次数据库以及自上而下地进行分布数据管理的功能，较好地解决了异构分布库系统的全局分布透明性和局部自治性的矛盾．本文主要介绍ＩＭＤＢＳ系统所采用的多源数据库模型、多源数据语言以及多源查询处理的实现技术．

大规模时序图数据的查询处理与挖掘技术综述

时序图作为一种带有时间维度的图结构,在图数据的查询处理与挖掘工作中扮演着越来越重要的角色.与传统的静态图不同,时序图的结构会随时间序列发生改变,即时序图的边由时间激活.而且由于时序图上每条边都有记录时间的标签,所以时序图包含的信息量相较于静态图也更为庞大,这使得现有的数据查询处理方法不能很好地应用于时序图中.因此如何解决时序图上的数据查询处理与挖掘问题得到研究者们的关注.对现有的时序图上的查询处理与挖掘方法进行了综述,详细介绍了时序图的应用背景和基本定义,梳理了现有的时序图模型,并从图查询处理方法、图挖掘方法和时序图管理系统3个方面对时序图上现有的工作进行了详细的介绍和分析.最后对时序图上可能的研究方向进行了展望,为相关研究提供参考.

分布式数据库查询处理和优化算法

分布式数据库系统是数据系统和计算机网络相互结合的产物,其主要目的就是实现计算机网络中数据分布和处理。如果查询过程处于分布式环境,因为查询涉及到的关系一般都被分片或者复制到多站点,所以计算代价的过程中不仅要全面考虑CPU和I/O的速度,还要全面考虑数据在站点之间通信过程中的网络传输代价。查询过程中的连接操作具有较高的通信代价,需要实现分布式数据库的有效处理连接,以便实现分布式查询处理的优化。

基于CORBA的工程数据库查询处理系统体系结构的研究

提出了基于的工程数据库查询处理系统的体系结构。该体系结构可以跨越硬件平台、跨越操作系统和异构数据库集成工程数据CORBA信息,便于实现查询的并行执行和异步处理,能够提高系统整体的执行效率。

ASP数据库标准查询模板设计

本文对 ASP数据库查询操作中的流程、参数和接口进行了标准化处理 .在此基础上 ,针对具有一定 ASP和 ADO编程经验的开发者 ,给出了一个数据库标准查询模板 .程序员只需对几个参数值加以设置 ,就可以构建一个完善的、适合不同应用环境的 Web数据库查询系统 .

数据统计分析查询的中间层语义缓存

数据库系统的应用越来越广泛，对查询的要求也越来越高。如何提高查询性能成为研究关键。本文对系统统计分析查询进行优化，缩短了查询反应时间。通过中间层语义缓存提高了数据统计分析性能。本文通过研究中间层语义缓存，提出一种适合数据统计分析查询的中间层语义缓存解决方案。

JDDataExchanger数据服务中间件：性能达国际先进水平

由广东商学院与广州航信计算机科技发展有限公司共同研究完成的“JDDataExchanger数据服务中间件”项目通过了省级科技成果鉴定。该项成果在异构海量数据查询处理、数据传输等性能方面达到国际先进水平。

Semantic-based query processing for relational data integration

To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,all relative tables are found and decomposed into minimal connectable units.Minimal connectable units are joined according to semantic queries to produce the semantically correct query plans.Algorithms for query rewriting and transforming are presented.Computational complexity of the algorithms is discussed.Under the worst case,the query decomposing algorithm can be finished in O（n2） time and the query rewriting algorithm requires O（nm） time.And the performance of the algorithms is verified by experiments,and experimental results show that when the length of query is less than 8,the query processing algorithms can provide satisfactory performance.

Data partitioning based on sampling for power load streams

A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,which is implemented as an extended reservoir-sampling algorithm.A skip factor based on the change ratio of data-values is introduced to describe the distribution characteristics of data-values adaptively.The second step of this method is to partition the fluxes of data streams averagely,which is implemented with two alternative equal-depth histogram generating algorithms that fit the different cases：one for incremental maintenance based on heuristics and the other for periodical updates to generate an approximate partition vector.The experimental results on actual data prove that the method is efficient,practical and suitable for time-varying data streams processing.

Top-K Query Framework in Wireless Sensor Networks for Smart Grid

The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer- interactive network that＇s supported by fine-grained monitoring. Large-scale WSNs （Wireless Sensor Networks） have been considered one of the very promising technologies to support the implementation of smart grid. WSNs are applied in almost every aspect of smart grid, including power generation, power transmission, power distribution, power utilization and power dispatch, and the data query processing of ＇WSNs in power grid＇ become an hotspot issue due to the amount of data of power grid is very large and the requirement of response time is very high. To meet the demands, top-k query processing is a good choice, which performs the cooperative query by aggregating the database objects＇ degree of match for each different query predicate and returning the best k matching objects. In this paper, a framework that can effectively apply top-k query to wireless sensor network in smart grid is proposed, which is based on the cluster-topology sensor network. In the new method, local indices are used to optimize the necessary query routing and process intermediate results inside the cluster to cut down the data traffic, and the hierarchical join query is executed based on the local results.Besides, top-k query results are verified by the clean-up process, and two schemes are taken to deal with the problem of node＇s dynamicity, which further reduce communication cost. Case studies and experimental results show that our algorithm has outperformed the current existing one with higher quality results and better efficiently.

Differentially Private Multidimensional Data Publication

Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guarantees. Privacy- preserving multidimensional data publishing currently lacks a solid theoretical foundation. It is urgent to develop new techniques with provable privacy guarantees, e-Differential privacy is the only method that can provide such guarantees. In this paper, we propose a multidimensional data publishing scheme that ensures c-differential privacy while providing accurate results for query processing. The proposed solution applies nonstandard wavelet transforms on the raw multidimensional data and adds noise to guarantee c-differential privacy. Then, the scheme processes arbitrarily queries directly in the noisy wavelet- coefficient synopses of relational tables and expands the noisy wavelet coefficients back into noisy relational tuples until the end result of the query. Moreover, experimental results demonstrate the high accuracy and effectiveness of our approach.

A method for improving graph queries processing using positional inverted index (P.I.I) idea in search engines and parallelization techniques

The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer set. These tables are implemented using column-based techniques and are used to store graphs of database, frequent sub-graphs and the neighborhood of nodes. In order to exact checking of remaining graphs, the vertex invariant is used for isomorphism test which can be parallel implemented. The results of evaluation indicate that proposed method outperforms existing methods.

Continuous query scheduler based on operators clustering

Data stream management system (DSMS) provides convenient solutions to the problem of processing continuous queries on data streams.Previous approaches for scheduling these queries and their operators assume that each operator runs in separate thread or all operators combine in one query plan and run in a single thread.Both approaches suffer from severe drawbacks concerning the thread overhead and the stalls due to expensive operators.To overcome these drawbacks,a novel approach called clustered operators scheduling (COS) is proposed that adaptively clusters operators of the query plan into a number of groups based on their selectivity and computing cost using S-mean clustering.Experimental evaluation is provided to demonstrate the potential benefits of COS scheduling over the other scheduling strategies.COS can provide adaptive,flexible,reliable,scalable and robust design for continuous query processor.

Bottom-up mining of XML query patterns to improve XML querying

Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results of frequent queries. We discover frequent query patterns from user-issued queries using an efficient bottom-up mining approach called VBUXMiner. VBUXMiner consists of two main steps. First, all queries are merged into a summary structure named "compressed global tree guide" (CGTG). Second, a bottom-up traversal scheme based on the CGTG is employed to generate frequent query patterns. We use the frequent query patterns in a cache mechanism to improve the XML query performance. Experimental results show that our proposed mining approach outperforms the previous mining algorithms for XML queries, such as XQPMinerTID and FastXMiner, and that by caching the results of frequent query patterns, XML query performance can be dramatically improved.